OpenRaiser/Intern-Atlas huggingface dataset:
Rethinking Textual Adversarial Defense for Pre-trained Language Models
Venue: arXiv cs.CL 2022
Year: 2022

Although pre-trained language models (PrLMs) have achieved significant success, recent studies demonstrate that PrLMs are vulnerable to adversarial attacks. By generating adversarial examples with slight perturbations on different levels (sentence / word / character), adversarial attacks can fool PrLMs to generate incorrect predictions, which questions the robustness of PrLMs. However, we find that most existing textual adversarial examples are unnatural, which can be easily distinguished by both human and machine. Based on a general anomaly detector, we propose a novel metric (Degree of Anomaly) as a constraint to enable current adversarial attack approaches to generate more natural and imperceptible adversarial examples. Under this new constraint, the success rate of existing attacks drastically decreases, which reveals that the robustness of PrLMs is not as fragile as they claimed. In addition, we find that four types of randomization can invalidate a large portion of textual adversarial examples. Based on anomaly detector and randomization, we design a universal defense framework, which is among the first to perform textual adversarial defense without knowing the specific attack. Empirical results show that our universal defense framework achieves comparable or even higher after-attack accuracy with other specific defenses, while preserving higher original accuracy at the same time. Our work discloses the essence of textual adversarial attacks, and indicates that (1) further works of adversarial attacks should focus more on how to overcome the detection and resist the randomization, otherwise their adversarial examples would be easily detected and invalidated; and (2) compared with the unnatural and perceptible adversarial examples, it is those undetectable adversarial examples that pose real risks for PrLMs and require more attention for future robustness-enhancing strategies.
A semantic web approach to uplift decentralized household energy data
Venue: arXiv cs.AI 2022
Year: 2022

In a decentralized household energy system comprised of various devices such as home appliances, electric vehicles, and solar panels, end-users are able to dig deeper into the system's details and further achieve energy sustainability if they are presented with data on the electric energy consumption and production at the granularity of the device. However, many databases in this field are siloed from other domains, including solely information pertaining to energy. This may result in the loss of information (e.g. weather) on each device's energy use. Meanwhile, a large number of these datasets have been extensively used in computational modeling techniques such as machine learning models. While such computational approaches achieve great accuracy and performance by concentrating only on a local view of datasets, model reliability cannot be guaranteed since such models are very vulnerable to data input fluctuations when information omission is taken into account. This article tackles the data isolation issue in the field of smart energy systems by examining Semantic Web methods on top of a household energy system. We offer an ontology-based approach for managing decentralized data at the device-level resolution in a system. As a consequence, the scope of the data associated with each device may easily be expanded in an interoperable manner throughout the Web, and additional information, such as weather, can be obtained from the Web, provided that the data is organized according to W3C standards.
Language-independence of DisCoCirc's Text Circuits: English and Urdu
Venue: arXiv cs.CL 2022
Year: 2022

DisCoCirc is a newly proposed framework for representing the grammar and semantics of texts using compositional, generative circuits. While it constitutes a development of the Categorical Distributional Compositional (DisCoCat) framework, it exposes radically new features. In particular, [14] suggested that DisCoCirc goes some way toward eliminating grammatical differences between languages. In this paper we provide a sketch that this is indeed the case for restricted fragments of English and Urdu. We first develop DisCoCirc for a fragment of Urdu, as it was done for English in [14]. There is a simple translation from English grammar to Urdu grammar, and vice versa. We then show that differences in grammatical structure between English and Urdu - primarily relating to the ordering of words and phrases - vanish when passing to DisCoCirc circuits.
LogStamp: Automatic Online Log Parsing Based on Sequence Labelling
Venue: arXiv cs.SE 2022
Year: 2022

Logs are one of the most critical data for service management. It contains rich runtime information for both services and users. Since size of logs are often enormous in size and have free handwritten constructions, a typical log-based analysis needs to parse logs into structured format first. However, we observe that most existing log parsing methods cannot parse logs online, which is essential for online services. In this paper, we present an automatic online log parsing method, name as LogStamp. We extensively evaluate LogStamp on five public datasets to demonstrate the effectiveness of our proposed method. The experiments show that our proposed method can achieve high accuracy with only a small portion of the training set. For example, it can achieve an average accuracy of 0.956 when using only 10% of the data training.
Contributions à l'asservissement visuel et à l'imagerie en médecine
Venue: arXiv cs.RO 2022
Year: 2022

This manuscript gives an overview of my research work carried out within the FEMTO-ST institute in Besançon, more particularly in the Automatic and Micro-Mechatronic Systems (AS2M) department. It is above all the result of my (co)-supervision of interns, PhD students and postdocs. I would like to pay tribute to them, for their major contribution to scientific research, here and elsewhere.
Semi-supervised classification using a supervised autoencoder for biomedical applications
Venue: arXiv cs.LG 2022
Year: 2022

In this paper we present a new approach to solve semi-supervised classification tasks for biomedical applications, involving a supervised autoencoder network. We create a network architecture that encodes labels into the latent space of an autoencoder, and define a global criterion combining classification and reconstruction losses. We train the Semi-Supervised AutoEncoder (SSAE) on labelled data using a double descent algorithm. Then, we classify unlabelled samples using the learned network thanks to a softmax classifier applied to the latent space which provides a classification confidence score for each class. We implemented our SSAE method using the PyTorch framework for the model, optimizer, schedulers, and loss functions. We compare our semi-supervised autoencoder method (SSAE) with classical semi-supervised methods such as Label Propagation and Label Spreading, and with a Fully Connected Neural Network (FCNN). Experiments show that the SSAE outperforms Label Propagation and Spreading and the Fully Connected Neural Network both on a synthetic dataset and on two real-world biological datasets.
Quantifying probabilistic robustness of tree-based classifiers against natural distortions
Venue: arXiv cs.LG 2022
Year: 2022

The concept of trustworthy AI has gained widespread attention lately. One of the aspects relevant to trustworthy AI is robustness of ML models. In this study, we show how to probabilistically quantify robustness against naturally occurring distortions of input data for tree-based classifiers under the assumption that the natural distortions can be described by multivariate probability distributions that can be transformed to multivariate normal distributions. The idea is to extract the decision rules of a trained tree-based classifier, separate the feature space into non-overlapping regions and determine the probability that a data sample with distortion returns its predicted label. The approach is based on the recently introduced measure of real-world-robustness, which works for all black box classifiers, but is only an approximation and only works if the input dimension is not too high, whereas our proposed method gives an exact measure.
On Deep Learning in Password Guessing, a Survey
Venue: arXiv cs.CR 2022
Year: 2022

The security of passwords is dependent on a thorough understanding of the strategies used by attackers. Unfortunately, real-world adversaries use pragmatic guessing tactics like dictionary attacks, which are difficult to simulate in password security research. Dictionary attacks must be carefully configured and modified to be representative of the actual threat. This approach, however, needs domain-specific knowledge and expertise that are difficult to duplicate. This paper compares various deep learning-based password guessing approaches that do not require domain knowledge or assumptions about users' password structures and combinations. The involved model categories are Recurrent Neural Networks, Generative Adversarial Networks, Autoencoder, and Attention mechanisms. Additionally, we proposed a promising research experimental design on using variations of IWGAN on password guessing under non-targeted offline attacks. Using these advanced strategies, we can enhance password security and create more accurate and efficient Password Strength Meters.
Dialogue Term Extraction using Transfer Learning and Topological Data Analysis
Venue: arXiv cs.CL 2022
Year: 2022

Goal oriented dialogue systems were originally designed as a natural language interface to a fixed data-set of entities that users might inquire about, further described by domain, slots, and values. As we move towards adaptable dialogue systems where knowledge about domains, slots, and values may change, there is an increasing need to automatically extract these terms from raw dialogues or related non-dialogue data on a large scale. In this paper, we take an important step in this direction by exploring different features that can enable systems to discover realizations of domains, slots, and values in dialogues in a purely data-driven fashion. The features that we examine stem from word embeddings, language modelling features, as well as topological features of the word embedding space. To examine the utility of each feature set, we train a seed model based on the widely used MultiWOZ data-set. Then, we apply this model to a different corpus, the Schema-Guided Dialogue data-set. Our method outperforms the previously proposed approach that relies solely on word embeddings. We also demonstrate that each of the features is responsible for discovering different kinds of content. We believe our results warrant further research towards ontology induction, and continued harnessing of topological data analysis for dialogue and natural language processing research.
DualVoice: Speech Interaction that Discriminates between Normal and Whispered Voice Input
Venue: arXiv cs.HC 2022
Year: 2022

Interactions based on automatic speech recognition (ASR) have become widely used, with speech input being increasingly utilized to create documents. However, as there is no easy way to distinguish between commands being issued and text required to be input in speech, misrecognitions are difficult to identify and correct, meaning that documents need to be manually edited and corrected. The input of symbols and commands is also challenging because these may be misrecognized as text letters. To address these problems, this study proposes a speech interaction method called DualVoice, by which commands can be input in a whispered voice and letters in a normal voice. The proposed method does not require any specialized hardware other than a regular microphone, enabling a complete hands-free interaction. The method can be used in a wide range of situations where speech recognition is already available, ranging from text input to mobile/wearable computing. Two neural networks were designed in this study, one for discriminating normal speech from whispered speech, and the second for recognizing whisper speech. A prototype of a text input system was then developed to show how normal and whispered voice can be used in speech text input. Other potential applications using DualVoice are also discussed.
Atrial Fibrillation Recurrence Risk Prediction from 12-lead ECG Recorded Pre- and Post-Ablation Procedure
Venue: arXiv cs.LG 2022
Year: 2022

Introduction: 12-lead electrocardiogram (ECG) is recorded during atrial fibrillation (AF) catheter ablation procedure (CAP). It is not easy to determine if CAP was successful without a long follow-up assessing for AF recurrence (AFR). Therefore, an AFR risk prediction algorithm could enable a better management of CAP patients. In this research, we extracted features from 12-lead ECG recorded before and after CAP and train an AFR risk prediction machine learning model. Methods: Pre- and post-CAP segments were extracted from 112 patients. The analysis included a signal quality criterion, heart rate variability and morphological biomarkers engineered from the 12-lead ECG (804 features overall). 43 out of the 112 patients (n) had AFR clinical endpoint available. These were utilized to assess the feasibility of AFR risk prediction, using either pre or post CAP features. A random forest classifier was trained within a nested cross validation framework. Results: 36 features were found statistically significant for distinguishing between the pre and post surgery states (n=112). For the classification, an area under the receiver operating characteristic (AUROC) curve was reported with AUROC_pre=0.64 and AUROC_post=0.74 (n=43). Discussion and conclusions: This preliminary analysis showed the feasibility of AFR risk prediction. Such a model could be used to improve CAP management.
Robotic Perception in Agri-food Manipulation: A Review
Venue: arXiv cs.RO 2022
Year: 2022

To better optimise the global food supply chain, robotic solutions are needed to automate tasks currently completed by humans. Namely, phenotyping, quality analysis and harvesting are all open problems in the field of agricultural robotics. Robotic perception is a key challenge for autonomous solutions to such problems as scene understanding and object detection are vital prerequisites to any grasping tasks that a robot may undertake. This work conducts a brief review of modern robot perception models and discusses their efficacy within the agri-food domain.
A Survey and Framework of Cooperative Perception: From Heterogeneous Singleton to Hierarchical Cooperation
Venue: arXiv cs.CV 2022
Year: 2022

Perceiving the environment is one of the most fundamental keys to enabling Cooperative Driving Automation (CDA), which is regarded as the revolutionary solution to addressing the safety, mobility, and sustainability issues of contemporary transportation systems. Although an unprecedented evolution is now happening in the area of computer vision for object perception, state-of-the-art perception methods are still struggling with sophisticated real-world traffic environments due to the inevitably physical occlusion and limited receptive field of single-vehicle systems. Based on multiple spatially separated perception nodes, Cooperative Perception (CP) is born to unlock the bottleneck of perception for driving automation. In this paper, we comprehensively review and analyze the research progress on CP and, to the best of our knowledge, this is the first time to propose a unified CP framework. Architectures and taxonomy of CP systems based on different types of sensors are reviewed to show a high-level description of the workflow and different structures for CP systems. Node structure, sensor modality, and fusion schemes are reviewed and analyzed with comprehensive literature to provide detailed explanations of specific methods. A Hierarchical CP framework is proposed, followed by a review of existing Datasets and Simulators to sketch an overall landscape of CP. Discussion highlights the current opportunities, open challenges, and anticipated future trends.
LEAPER: Fast and Accurate FPGA-based System Performance Prediction via Transfer Learning
Venue: arXiv cs.AR 2022
Year: 2022

Machine learning has recently gained traction as a way to overcome the slow accelerator generation and implementation process on an FPGA. It can be used to build performance and resource usage models that enable fast early-stage design space exploration. First, training requires large amounts of data (features extracted from design synthesis and implementation tools), which is cost-inefficient because of the time-consuming accelerator design and implementation process. Second, a model trained for a specific environment cannot predict performance or resource usage for a new, unknown environment. In a cloud system, renting a platform for data collection to build an ML model can significantly increase the total-cost-ownership (TCO) of a system. Third, ML-based models trained using a limited number of samples are prone to overfitting. To overcome these limitations, we propose LEAPER, a transfer learning-based approach for prediction of performance and resource usage in FPGA-based systems. The key idea of LEAPER is to transfer an ML-based performance and resource usage model trained for a low-end edge environment to a new, high-end cloud environment to provide fast and accurate predictions for accelerator implementation. Experimental results show that LEAPER (1) provides, on average across six workloads and five FPGAs, 85% accuracy when we use our transferred model for prediction in a cloud environment with 5-shot learning and (2) reduces design-space exploration time for accelerator implementation on an FPGA by 10x, from days to only a few hours.
Leaning-Based Control of an Immersive-Telepresence Robot
Venue: arXiv cs.RO 2022
Year: 2022

In this paper, we present an implementation of a leaning-based control of a differential drive telepresence robot and a user study in simulation, with the goal of bringing the same functionality to a real telepresence robot. The participants used a balance board to control the robot and viewed the virtual environment through a head-mounted display. The main motivation for using a balance board as the control device stems from Virtual Reality (VR) sickness; even small movements of your own body matching the motions seen on the screen decrease the sensory conflict between vision and vestibular organs, which lies at the heart of most theories regarding the onset of VR sickness. To test the hypothesis that the balance board as a control method would be less sickening than using joysticks, we designed a user study (N=32, 15 women) in which the participants drove a simulated differential drive robot in a virtual environment with either a Nintendo Wii Balance Board or joysticks. However, our pre-registered main hypotheses were not supported; the joystick did not cause any more VR sickness on the participants than the balance board, and the board proved to be statistically significantly more difficult to use, both subjectively and objectively. Analyzing the open-ended questions revealed these results to be likely connected, meaning that the difficulty of use seemed to affect sickness; even unlimited training time before the test did not make the use as easy as the familiar joystick. Thus, making the board easier to use is a key to enable its potential; we present a few possibilities towards this goal.
Anatomy-Aware Contrastive Representation Learning for Fetal Ultrasound
Venue: arXiv cs.CV 2022
Year: 2022

Self-supervised contrastive representation learning offers the advantage of learning meaningful visual representations from unlabeled medical datasets for transfer learning. However, applying current contrastive learning approaches to medical data without considering its domain-specific anatomical characteristics may lead to visual representations that are inconsistent in appearance and semantics. In this paper, we propose to improve visual representations of medical images via anatomy-aware contrastive learning (AWCL), which incorporates anatomy information to augment the positive/negative pair sampling in a contrastive learning manner. The proposed approach is demonstrated for automated fetal ultrasound imaging tasks, enabling the positive pairs from the same or different ultrasound scans that are anatomically similar to be pulled together and thus improving the representation learning. We empirically investigate the effect of inclusion of anatomy information with coarse- and fine-grained granularity, for contrastive learning and find that learning with fine-grained anatomy information which preserves intra-class difference is more effective than its counterpart. We also analyze the impact of anatomy ratio on our AWCL framework and find that using more distinct but anatomically similar samples to compose positive pairs results in better quality representations. Experiments on a large-scale fetal ultrasound dataset demonstrate that our approach is effective for learning representations that transfer well to three clinical downstream tasks, and achieves superior performance compared to ImageNet supervised and the current state-of-the-art contrastive learning methods. In particular, AWCL outperforms ImageNet supervised method by 13.8% and state-of-the-art contrastive-based method by 7.1% on a cross-domain segmentation task.
The Robustness of Tether Friction in Non-idealized Terrains
Venue: arXiv cs.RO 2022
Year: 2022

Reduced traction limits the ability of mobile robotic systems to resist or apply large external loads, such as tugging a massive payload. One simple and versatile solution is to wrap a tether around naturally occurring objects to leverage the capstan effect and create exponentially-amplified holding forces. Experiments show that an idealized capstan model explains force amplification experienced on common irregular outdoor objects - trees, rocks, posts. Robust to variable environmental conditions, this exponential amplification method can harness single or multiple capstan objects, either in series or in parallel with a team of robots. This adaptability allows for a range of potential configurations especially useful for when objects cannot be fully encircled or gripped. These principles are demonstrated with mobile platforms to (1) control the lowering and arrest of a payload, (2) to achieve planar control of a payload, and (3) to act as an anchor point for a more massive platform to winch towards. We show the simple addition of a tether, wrapped around shallow stones in sand, amplifies holding force of a low-traction platform by up to 774x.
Decentralized Collaborative Learning with Probabilistic Data Protection
Venue: arXiv cs.LG 2022
Year: 2022

We discuss future directions of Blockchain as a collaborative value co-creation platform, in which network participants can gain extra insights that cannot be accessed when disconnected from the others. As such, we propose a decentralized machine learning framework that is carefully designed to respect the values of democracy, diversity, and privacy. Specifically, we propose a federated multi-task learning framework that integrates a privacy-preserving dynamic consensus algorithm. We show that a specific network topology called the expander graph dramatically improves the scalability of global consensus building. We conclude the paper by making some remarks on open problems.